Performance metrics for traditional and context-aware big data recommender systems

Performance metrics for traditional and context-aware big data recommender systems

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Recommender System (RS) concept was coined in the mid-1990s, when researchers took interest in recommendation problems that primarily used the concept of ratings to obtain the user preferences for different items. A lot of work has been exercised and investigated in this area for recommending the most relevant information and contents to users without taking the contextual information, such as date, time, location and event. In the last few years, context-aware recommender systems (CARS) have made tremendous contributions in all domains of life and improved the recommendation process based on the contextual information along with the traditional approaches. The effectiveness of an algorithm can be measured in the sense that how efficiently it returns the recommendation to users/customers with respect to context or occasion. To assess the effectiveness and performance of any recommender algorithms completely, some common metrics are defined to assess the performance of the recommender algorithm beforehand.

Chapter Contents:

  • 4.1 Introduction
  • 4.2 CARS—a brief overview
  • 4.3 Evaluation of RSs
  • 4.3.1 Evaluation metrics
  • Prediction accuracy metrics
  • Usage prediction measurement/classifying accuracy metrics
  • Rank accuracy metrics
  • 4.4 Diversity and accuracy metrics used in CARS
  • 4.4.1 How recommendation accuracy is measured in CARS?
  • 4.4.2 Diversity measurement in CARS
  • 4.5 How to choose an appropriate evaluation metrics?
  • 4.6 Conclusion
  • Acknowledgments
  • References

Inspec keywords: Big Data; ubiquitous computing; recommender systems

Other keywords: CARS; contextual information; recommender systems; performance metrics; context-aware Big Data; RS

Subjects: Information networks; Data handling techniques; Search engines

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